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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: whisper-meidum-ko-normalized-1273h
    results: []

whisper-medium-ko-normalized-1273h

This model is a fine-tuned version of openai/whisper-medium on a custom dataset for improving Korean speech recognition. It achieves the following results on the evaluation set:

  • Loss: 0.1254
  • Wer: 0.0551

Model description

The model was a fine-tuned version of openai/whisper-medium transcript the Korean audio sources into text. It was trained on GCP's a2-highgpu-1g (a100-40G) for 26 hours with about $90.

Intended uses & limitations

This model was trained to extend the performance of the original whisper model for Korean transcription task.

Training and evaluation data

I downloaded all data from AI-HUB (https://aihub.or.kr/). Two datasets, in particular, caught my attention: "Instruction Audio Set" and "Noisy Conversation Audio Set". Following indicates the hours information for each dastset.

dataset name train_split (hours) validation_split (hours)
Instruction Audio Set 910 105
Noisy Conversation Audio Set 363 76

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 24
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0588 1.0 8775 0.1225 0.0604
0.0287 2.0 17550 0.1186 0.0567
0.0148 3.0 26325 0.1254 0.0551

Framework versions

  • Transformers 4.28.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.2

Evaluation Result for the dataset google/fleurs

The trained model is evaluated on the test split of subset ko_kr from the dataset google/fleurs. Please note that the model was not trained on the train split from the dataset.

model Wer
openai/whisper 0.2469
this model 0.2189